Obesity is closely related to the occurrence and development of dilated cardiomyopathy (DCM). Differences in clinical characteristics of dilated cardiomyopathy patients with distinct weight status and the prognostic value of weight management have not been clarified.
To explore the baseline clinical characteristics of DCM patients with different weight statuses, and to analyze the impact of weight management on their prognosis.
This was a single-center prospective cohort study. A total of 322 obese patients with DCM admitted to the Affiliated Hospital of Jiangsu University from January 2022 to June 2024 were prospectively collected. DCM patients were assigned into the normal group (BMI<24 kg/m2), overweight group (24 kg/m2≤BMI<28 kg/m2) and obese group (BMI≥28 kg/m2) according to the BMI. Baseline characteristics were collected. They were followed up for 12 months on telephone or outpatient visits. The incidence of major adverse cardiovascular events (MACEs) was recorded. According to the weight change during the 12-month weight management, they were divided into weight change <5%, 5%≤weight change <10% and≥10% weight change groups. Plots of MACEs among the three groups and Kaplan-Meier survival curves were plots. Univariate and multivariate Cox regression and subgroup analyses were conducted to identify influlencing factors for MACEs in DCM patients.
DCM patients were divided into the normal group (84 cases), overweight group (132 cases) and obese group (106 cases) according to baseline BMI. There were significant differences in age, systolic blood pressure, diastolic blood pressure, left ventricular end-systolic diameter (LVSd), comorbidities (hypertension, diabetes, coronary atherosclerosis), lifestyle (smoking history), and drug use [orlistat, glucagon-like peptide 1 (GLP-1) agonists, soluble guanylate cyclase (GC) agonists] among the three groups (all P<0.05). Based on the magnitude of body weight change over 12 months, participants were categorized into three groups: weight change <5% (n=115), 5%≤weight change <10% (n=157), and ≥10% weight change (n=50) groups. There were significant differences in admission body weight, follow-up brain natriuretic peptide (BNP), follow-up left ventricular ejection fraction (LVEF), follow-up cardiac function, follow-up MACEs and GLP-1 agonist use among the weight change <5%, 5%≤weight change<10% and≥10% weight change groups (P<0.05). The range of weight change during the 12-month follow-up was linearly related to follow-up BNP (rs=-0.158, P=0.004) and LVEF (rs=0.229, P<0.001). The Kaplan-Meier survival curve showed a significant difference in the incidence of MACEs among the weight change <5%, 5%≤weight change <10% and≥10% weight change groups (χ2=16.83, P<0.001). Univariate Cox proportional hazards regression model analysis showed that follow-up BNP, LVEF, follow-up cardiac function, weight change, and the use of GLP-1 receptor agonists, mineralocorticoid receptor antagonist (MRA), and sodium-glucose cotransporter-2 inhibitor (SGLT2i) were independent influencing factors for MACEs in DCM patients (P<0.05). After adjusting for gender, diabetes, smoking history, drinking history, and drug use, MACEs was the dependent variable and weight change was the independent variable. Multivariate Cox proportional hazards regression model showed that weight change was independently related to the occurrence of MACEs in DCM patients (P<0.05). Subgroup analysis results showed that increased weight change was significantly associated with a reduced risk of MACEs (HRoverall=0.89, 95%CI=0.81-0.98, P=0.018). The interaction analysis showed the increase in weight change was consistent with the risk of MACEs in DCM patients stratified by gender, age, diabetes, and use of SGLT2i, MRA or GLP-1 receptor agonists (Pinteraction>0.05), all showing a protective effect. The association between weight change and the risk of MACEs in DCM patients was significantly different among patients who used β-blockers or not (Pinteraction =0.004).
DCM patients with a BMI≥24 kg/m2 are younger and more likely to have metabolic disorders like hypertension and diabetes. After 12 months of weight management, DCM patients with a weight loss of≥10% have the most significant improvement in cardiac function, manifesting as significantly decreased BNP and increased LVEF at follow-up, and the lowest incidence of MACEs. Structured weight management with the goal of weight loss ≥10% is therefore recommended to be included in the comprehensive treatment of overweight/obese DCM patients to improve their cardiac function and clinical prognosis.
Cardiometabolic multimorbidity (CMM) represents one of the most prevalent and stable multimorbidity patterns. Relative fat mass (RFM), as a novel anthropometric indicator for assessing adiposity, has shown promise as a predictor of individual cardiometabolic diseases. However, evidence regarding its association with the risk of CMM remains limited.
To investigate the association between RFM and the risk of CMM across different genders, and to evaluate the potential role of RFM in the prevention and management of CMM.
A total of 116 321 permanent residents from 12 urban communities (including Suzhou) were selected as study participants from March 2017 to July 2021. Based on gender and CMM status, participants were stratified into CMM and non-CMM groups. Baseline characteristics were compared between these groups separately for each gender. Multivariable Logistic regression analysis was employed to examine the association between RFM and the risk of CMM stratified by sex. Restricted cubic spline (RCS) curves were applied to explore potential non-linear relationships. Subgroup analyses and interaction tests were conducted to investigate variations in the association across different populations.
A total of 116 321 participants were included in this study. Among them, 46 637 (40.1%) were male, with 11 969 cases (25.7%) in the CMM group and 34 668 cases (74.3%) in the non-CMM group. While 69 684 (59.9%) were female, with 16 668 cases (23.9%) in the CMM group and 53 016 cases (76.1%) in the non-CMM group. RFM levels were significantly higher in the CMM group than in the non-CMM group for both sexes(P<0.001). After adjusting for confounders including age,education level, smoking, alcohol consumption, body mass index (BMI), low-density lipoprotein cholesterol (LDL-C), remnant cholesterol, blood glucose, systolic blood pressure, and diastolic blood pressure, multivariable Logistic regression analysis revealed that among males, the risks of CMM in the T2 to T4 groups were 1.530, 2.086, and 2.945 times that of T1 group, respectively (P<0.001). Among females, the risks of CMM in the F2 to F4 groups were 1.205, 1.532, and 1.760 times that of F1 group, respectively (P<0.001). Furthermore, for each unit increase in RFM, the risk of CMM increased by 1.109 times in males (OR=1.109, 95%CI=1.101-1.116, P<0.001) and by 1.054 times in females (OR=1.054, 95%CI=1.049-1.060, P<0.001). RCS analysis demonstrated a nonlinear relationship between RFM and CMM risk in both sexes. For males,the inflection point of OR=1 was 25.26 (Pnonlinearity <0.001). For females, the inflection point of OR=1 was 38.41 (Pnonlinearity=0.001). Subgroup analysis showed that the risk of RFM and CMM was significantly associated with male (OR=1.108, 95%CI=1.101-1.115), age≥45 years old (OR=1.011, 95%CI=1.008-1.013), less than high school education (OR=1.013, 95%CI=1.011-1.015), current smoking (OR=1.062, 95%CI=1.054-1.069), current drinking (OR=1.021, 95%CI=1.015-1.028) and BMI<24 kg/m2 (OR=1.010, 95%CI=1.007-1.014). The results of interaction analysis showed that the association between RFM and the risk of CMM was affected by the interaction between gender, age, education level, smoking, drinking and BMI (Pinteraction<0.05).
Higher RFM is significantly associated with an increased risk of CMM, and this association is more pronounced in males, individuals aged≥45 years, those with a high school education or below, smokers, drinkers, and individuals with a BMI<24 kg/m2.
